Pavan Kumar
3 weeks ago
committed by
GitHub
commit
962d12f371
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"""
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CEO -> Finds department leader
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Department leader -> Finds employees
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Employees -> Do the work
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Todo
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- Create schemas that enable the ceo to find the department leader or leaders
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- CEO then distributes orders to department leaders or just one leader
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- Department leader then distributes orders to employees
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- Employees can choose to do the work or delegate to another employee or work together
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- When the employees are done, they report back to the department leader
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- Department leader then reports back to the ceo
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- CEO then reports back to the user
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Logic
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- dynamically setup conversations for each department -- Feed context to each agent in the department
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- Feed context to each agent in the department
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"""
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from typing import Callable, List, Union
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from pydantic import BaseModel, Field
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from swarms.structs.agent import Agent
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from swarms.structs.conversation import Conversation
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from swarms.structs.ma_utils import list_all_agents
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from swarms.utils.str_to_dict import str_to_dict
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from swarms.utils.any_to_str import any_to_str
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class Department(BaseModel):
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name: str = Field(description="The name of the department")
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description: str = Field(
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description="A description of the department"
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)
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employees: List[Union[Agent, Callable]] = Field(
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description="A list of employees in the department"
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)
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leader_name: str = Field(
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description="The name of the leader of the department"
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)
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class Config:
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arbitrary_types_allowed = True
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CEO_SCHEMA = {
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"name": "delegate_task_to_department",
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"description": "CEO function to analyze and delegate tasks to appropriate department leaders",
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"parameters": {
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"type": "object",
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"properties": {
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"thought": {
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"type": "string",
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"description": "Reasoning about the task, its requirements, and potential approaches",
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},
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"plan": {
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"type": "string",
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"description": "Structured plan for how to accomplish the task across departments",
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},
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"tasks": {
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"type": "object",
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"properties": {
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"task_description": {"type": "string"},
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"selected_departments": {
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"type": "array",
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"items": {"type": "string"},
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"description": "List of department names that should handle this task",
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},
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"selected_leaders": {
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"type": "array",
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"items": {"type": "string"},
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"description": "List of department leaders to assign the task to",
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},
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"success_criteria": {"type": "string"},
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},
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"required": [
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"task_description",
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"selected_departments",
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"selected_leaders",
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],
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},
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},
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"required": ["thought", "plan", "tasks"],
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},
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}
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DEPARTMENT_LEADER_SCHEMA = {
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"name": "manage_department_task",
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"description": "Department leader function to break down and assign tasks to employees",
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"parameters": {
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"type": "object",
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"properties": {
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"task_management": {
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"type": "object",
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"properties": {
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"original_task": {"type": "string"},
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"subtasks": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"subtask_id": {"type": "string"},
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"description": {"type": "string"},
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"assigned_employees": {
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"type": "array",
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"items": {"type": "string"},
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},
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"estimated_duration": {
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"type": "string"
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},
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"dependencies": {
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"type": "array",
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"items": {"type": "string"},
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},
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},
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},
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},
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"progress_tracking": {
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"type": "object",
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"properties": {
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"status": {
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"type": "string",
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"enum": [
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"not_started",
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"in_progress",
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"completed",
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],
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},
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"completion_percentage": {
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"type": "number"
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},
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"blockers": {
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"type": "array",
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"items": {"type": "string"},
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},
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},
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},
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},
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"required": ["original_task", "subtasks"],
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}
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},
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"required": ["task_management"],
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},
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}
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EMPLOYEE_SCHEMA = {
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"name": "handle_assigned_task",
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"description": "Employee function to process and execute assigned tasks",
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"parameters": {
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"type": "object",
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"properties": {
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"thought": {
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"type": "string",
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"description": "Reasoning about the task, its requirements, and potential approaches",
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},
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"plan": {
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"type": "string",
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"description": "Structured plan for how to accomplish the task across departments",
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},
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"task_execution": {
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"type": "object",
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"properties": {
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"subtask_id": {"type": "string"},
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"action_taken": {
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"type": "string",
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"enum": [
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"execute",
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"delegate",
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"collaborate",
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],
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},
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"execution_details": {
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"type": "object",
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"properties": {
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"status": {
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"type": "string",
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"enum": [
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"in_progress",
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"completed",
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"blocked",
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],
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},
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"work_log": {"type": "string"},
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"collaboration_partners": {
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"type": "array",
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"items": {"type": "string"},
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},
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"delegate_to": {"type": "string"},
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"results": {"type": "string"},
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"issues_encountered": {
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"type": "array",
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"items": {"type": "string"},
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},
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},
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},
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},
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"required": [
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"thought",
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"plan",
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"subtask_id",
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"action_taken",
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"execution_details",
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],
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},
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},
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"required": ["task_execution"],
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},
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}
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# Status report schemas for the feedback loop
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EMPLOYEE_REPORT_SCHEMA = {
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"name": "submit_task_report",
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"description": "Employee function to report task completion status to department leader",
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"parameters": {
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"type": "object",
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"properties": {
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"task_report": {
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"type": "object",
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"properties": {
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"subtask_id": {"type": "string"},
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"completion_status": {
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"type": "string",
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"enum": ["completed", "partial", "blocked"],
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},
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"work_summary": {"type": "string"},
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"time_spent": {"type": "string"},
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"challenges": {
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"type": "array",
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"items": {"type": "string"},
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},
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"next_steps": {"type": "string"},
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},
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"required": [
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"subtask_id",
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"completion_status",
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"work_summary",
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],
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}
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},
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"required": ["task_report"],
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},
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}
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DEPARTMENT_REPORT_SCHEMA = {
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"name": "submit_department_report",
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"description": "Department leader function to report department progress to CEO",
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"parameters": {
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"type": "object",
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"properties": {
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"department_report": {
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"type": "object",
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"properties": {
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"department_name": {"type": "string"},
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"task_summary": {"type": "string"},
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"overall_status": {
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"type": "string",
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"enum": ["on_track", "at_risk", "completed"],
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},
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"completion_percentage": {"type": "number"},
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"key_achievements": {
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"type": "array",
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"items": {"type": "string"},
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},
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"blockers": {
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"type": "array",
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"items": {"type": "string"},
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},
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"resource_needs": {
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"type": "array",
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"items": {"type": "string"},
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},
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"next_milestones": {
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"type": "array",
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"items": {"type": "string"},
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},
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},
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"required": [
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"department_name",
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"task_summary",
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"overall_status",
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],
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}
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},
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"required": ["department_report"],
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},
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}
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CEO_FINAL_REPORT_SCHEMA = {
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"name": "generate_final_report",
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"description": "CEO function to compile final report for the user",
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"parameters": {
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"type": "object",
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"properties": {
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"final_report": {
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"type": "object",
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"properties": {
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"task_overview": {"type": "string"},
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"overall_status": {
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"type": "string",
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"enum": ["successful", "partial", "failed"],
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},
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"department_summaries": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"department": {"type": "string"},
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"contribution": {"type": "string"},
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"performance": {"type": "string"},
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},
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},
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},
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"final_results": {"type": "string"},
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"recommendations": {
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"type": "array",
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"items": {"type": "string"},
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},
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"next_steps": {"type": "string"},
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},
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"required": [
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"task_overview",
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"overall_status",
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"final_results",
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],
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}
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},
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"required": ["final_report"],
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},
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}
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# # Example output schemas
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# CEO_EXAMPLE_OUTPUT = {
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# "thought": "This task requires coordination between the engineering and design departments to create a new feature. The engineering team will handle the backend implementation while design focuses on the user interface.",
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# "plan": "1. Assign backend development to engineering department\n2. Assign UI/UX design to design department\n3. Set up regular sync meetings between departments\n4. Establish clear success criteria",
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# "tasks": {
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# "task_description": "Develop a new user authentication system with social login integration",
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# "selected_departments": ["engineering", "design"],
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# "selected_leaders": ["engineering_lead", "design_lead"],
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# "success_criteria": "1. Social login working with 3 major providers\n2. UI/UX approved by design team\n3. Security audit passed\n4. Performance metrics met"
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# }
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# }
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# DEPARTMENT_LEADER_EXAMPLE_OUTPUT = {
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# "task_management": {
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# "original_task": "Develop a new user authentication system with social login integration",
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# "subtasks": [
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# {
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# "subtask_id": "ENG-001",
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# "description": "Implement OAuth2 integration for Google",
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# "assigned_employees": ["dev1", "dev2"],
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# "estimated_duration": "3 days",
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# "dependencies": ["DES-001"]
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# },
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# {
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# "subtask_id": "ENG-002",
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# "description": "Implement OAuth2 integration for Facebook",
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# "assigned_employees": ["dev3"],
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# "estimated_duration": "2 days",
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# "dependencies": ["DES-001"]
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# }
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# ],
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# "progress_tracking": {
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# "status": "in_progress",
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# "completion_percentage": 0.3,
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# "blockers": ["Waiting for design team to provide UI mockups"]
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# }
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# }
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# }
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# EMPLOYEE_EXAMPLE_OUTPUT = {
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# "thought": "The Google OAuth2 integration requires careful handling of token management and user data synchronization",
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# "plan": "1. Set up Google OAuth2 credentials\n2. Implement token refresh mechanism\n3. Create user data sync pipeline\n4. Add error handling and logging",
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# "task_execution": {
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# "subtask_id": "ENG-001",
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# "action_taken": "execute",
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# "execution_details": {
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# "status": "in_progress",
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# "work_log": "Completed OAuth2 credential setup and initial token handling implementation",
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# "collaboration_partners": ["dev2"],
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# "delegate_to": None,
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# "results": "Successfully implemented basic OAuth2 flow",
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# "issues_encountered": ["Need to handle token refresh edge cases"]
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||||||
# }
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# }
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# }
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# EMPLOYEE_REPORT_EXAMPLE = {
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# "task_report": {
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# "subtask_id": "ENG-001",
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# "completion_status": "partial",
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# "work_summary": "Completed initial OAuth2 implementation, working on token refresh mechanism",
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# "time_spent": "2 days",
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# "challenges": ["Token refresh edge cases", "Rate limiting considerations"],
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||||||
# "next_steps": "Implement token refresh mechanism and add rate limiting protection"
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||||||
# }
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||||||
# }
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# DEPARTMENT_REPORT_EXAMPLE = {
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||||||
# "department_report": {
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# "department_name": "Engineering",
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||||||
# "task_summary": "Making good progress on OAuth2 implementation, but waiting on design team for UI components",
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||||||
# "overall_status": "on_track",
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||||||
# "completion_percentage": 0.4,
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|
||||||
# "key_achievements": [
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||||||
# "Completed Google OAuth2 basic flow",
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|
||||||
# "Set up secure token storage"
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|
||||||
# ],
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|
||||||
# "blockers": ["Waiting for UI mockups from design team"],
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|
||||||
# "resource_needs": ["Additional QA resources for testing"],
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|
||||||
# "next_milestones": [
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|
||||||
# "Complete Facebook OAuth2 integration",
|
|
||||||
# "Implement token refresh mechanism"
|
|
||||||
# ]
|
|
||||||
# }
|
|
||||||
# }
|
|
||||||
|
|
||||||
# CEO_FINAL_REPORT_EXAMPLE = {
|
|
||||||
# "final_report": {
|
|
||||||
# "task_overview": "Successfully implemented new authentication system with social login capabilities",
|
|
||||||
# "overall_status": "successful",
|
|
||||||
# "department_summaries": [
|
|
||||||
# {
|
|
||||||
# "department": "Engineering",
|
|
||||||
# "contribution": "Implemented secure OAuth2 integrations and token management",
|
|
||||||
# "performance": "Excellent - completed all technical requirements"
|
|
||||||
# },
|
|
||||||
# {
|
|
||||||
# "department": "Design",
|
|
||||||
# "contribution": "Created intuitive UI/UX for authentication flows",
|
|
||||||
# "performance": "Good - delivered all required designs on time"
|
|
||||||
# }
|
|
||||||
# ],
|
|
||||||
# "final_results": "New authentication system is live and processing 1000+ logins per day",
|
|
||||||
# "recommendations": [
|
|
||||||
# "Add more social login providers",
|
|
||||||
# "Implement biometric authentication",
|
|
||||||
# "Add two-factor authentication"
|
|
||||||
# ],
|
|
||||||
# "next_steps": "Monitor system performance and gather user feedback for improvements"
|
|
||||||
# }
|
|
||||||
# }
|
|
||||||
|
|
||||||
|
|
||||||
class AutoCorp:
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
name: str = "AutoCorp",
|
|
||||||
description: str = "A company that uses agents to automate tasks",
|
|
||||||
departments: List[Department] = [],
|
|
||||||
ceo: Agent = None,
|
|
||||||
):
|
|
||||||
self.name = name
|
|
||||||
self.description = description
|
|
||||||
self.departments = departments
|
|
||||||
self.ceo = ceo
|
|
||||||
self.conversation = Conversation()
|
|
||||||
|
|
||||||
# Check if the CEO and departments are set
|
|
||||||
self.reliability_check()
|
|
||||||
|
|
||||||
# Add departments to conversation
|
|
||||||
self.add_departments_to_conversation()
|
|
||||||
|
|
||||||
# Initialize the CEO agent
|
|
||||||
self.initialize_ceo_agent()
|
|
||||||
|
|
||||||
# Initialize the department leaders
|
|
||||||
self.setup_department_leaders()
|
|
||||||
|
|
||||||
# Initialize the department employees
|
|
||||||
self.department_employees_initialize()
|
|
||||||
|
|
||||||
def initialize_ceo_agent(self):
|
|
||||||
self.ceo.tools_list_dictionary = [
|
|
||||||
CEO_SCHEMA,
|
|
||||||
CEO_FINAL_REPORT_SCHEMA,
|
|
||||||
]
|
|
||||||
|
|
||||||
def setup_department_leaders(self):
|
|
||||||
self.department_leader_initialize()
|
|
||||||
self.initialize_department_leaders()
|
|
||||||
|
|
||||||
def department_leader_initialize(self):
|
|
||||||
"""Initialize each department leader with their department's context."""
|
|
||||||
|
|
||||||
for department in self.departments:
|
|
||||||
# Create a context dictionary for the department
|
|
||||||
department_context = {
|
|
||||||
"name": department.name,
|
|
||||||
"description": department.description,
|
|
||||||
"employees": list_all_agents(
|
|
||||||
department.employees,
|
|
||||||
self.conversation,
|
|
||||||
department.name,
|
|
||||||
False,
|
|
||||||
),
|
|
||||||
}
|
|
||||||
|
|
||||||
# Convert the context to a string
|
|
||||||
context_str = any_to_str(department_context)
|
|
||||||
|
|
||||||
# TODO: Add the department leader's tools and context
|
|
||||||
department.leader.system_prompt += f"""
|
|
||||||
You are the leader of the {department.name} department.
|
|
||||||
|
|
||||||
Department Context:
|
|
||||||
{context_str}
|
|
||||||
|
|
||||||
Your role is to:
|
|
||||||
1. Break down tasks into subtasks
|
|
||||||
2. Assign subtasks to appropriate employees
|
|
||||||
3. Track progress and manage blockers
|
|
||||||
4. Report back to the CEO
|
|
||||||
|
|
||||||
Use the provided tools to manage your department effectively.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def department_employees_initialize(self):
|
|
||||||
"""Initialize each department leader with their department's context."""
|
|
||||||
|
|
||||||
for department in self.departments:
|
|
||||||
# Create a context dictionary for the department
|
|
||||||
department_context = {
|
|
||||||
"name": department.name,
|
|
||||||
"description": department.description,
|
|
||||||
"employees": list_all_agents(
|
|
||||||
department.employees,
|
|
||||||
self.conversation,
|
|
||||||
department.name,
|
|
||||||
False,
|
|
||||||
),
|
|
||||||
"leader": department.leader_name,
|
|
||||||
}
|
|
||||||
|
|
||||||
print(department_context)
|
|
||||||
|
|
||||||
# Convert the context to a string
|
|
||||||
context_str = any_to_str(department_context)
|
|
||||||
|
|
||||||
# Set the department leader's tools and context
|
|
||||||
department.employees.system_prompt += f"""
|
|
||||||
You are an employee of the {department.name} department.
|
|
||||||
|
|
||||||
Department Context:
|
|
||||||
{context_str}
|
|
||||||
|
|
||||||
Your role is to:
|
|
||||||
1. Break down tasks into subtasks
|
|
||||||
2. Assign subtasks to appropriate employees
|
|
||||||
3. Track progress and manage blockers
|
|
||||||
4. Report back to the CEO
|
|
||||||
|
|
||||||
Use the provided tools to manage your department effectively.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def initialize_department_leaders(self):
|
|
||||||
# Use list comprehension for faster initialization
|
|
||||||
[
|
|
||||||
setattr(
|
|
||||||
dept.leader,
|
|
||||||
"tools_list_dictionary",
|
|
||||||
[DEPARTMENT_LEADER_SCHEMA],
|
|
||||||
)
|
|
||||||
for dept in self.departments
|
|
||||||
]
|
|
||||||
|
|
||||||
def reliability_check(self):
|
|
||||||
if self.ceo is None:
|
|
||||||
raise ValueError("CEO is not set")
|
|
||||||
|
|
||||||
if self.departments is None:
|
|
||||||
raise ValueError("No departments are set")
|
|
||||||
|
|
||||||
if len(self.departments) == 0:
|
|
||||||
raise ValueError("No departments are set")
|
|
||||||
|
|
||||||
def add_departments_to_conversation(self):
|
|
||||||
# Batch process departments using list comprehension
|
|
||||||
messages = [
|
|
||||||
{
|
|
||||||
"role": "System",
|
|
||||||
"content": f"Team: {dept.name}\nDescription: {dept.description}\nLeader: {dept.leader_name}\nAgents: {list_all_agents(dept.employees, self.conversation, dept.name, False)}",
|
|
||||||
}
|
|
||||||
for dept in self.departments
|
|
||||||
]
|
|
||||||
self.conversation.batch_add(messages)
|
|
||||||
|
|
||||||
# def add_department(self, department: Department):
|
|
||||||
# self.departments.append(department)
|
|
||||||
|
|
||||||
# def add_employee(self, employee: Union[Agent, Callable]):
|
|
||||||
# self.departments[-1].employees.append(employee)
|
|
||||||
|
|
||||||
# def add_ceo(self, ceo: Agent):
|
|
||||||
# self.ceo = ceo
|
|
||||||
|
|
||||||
# def add_employee_to_department(
|
|
||||||
# self, employee: Union[Agent, Callable], department: Department
|
|
||||||
# ):
|
|
||||||
# department.employees.append(employee)
|
|
||||||
|
|
||||||
# def add_leader_to_department(
|
|
||||||
# self, leader: Agent, department: Department
|
|
||||||
# ):
|
|
||||||
# department.leader = leader
|
|
||||||
|
|
||||||
# def add_department_to_auto_corp(self, department: Department):
|
|
||||||
# self.departments.append(department)
|
|
||||||
|
|
||||||
# def add_ceo_to_auto_corp(self, ceo: Agent):
|
|
||||||
# self.ceo = ceo
|
|
||||||
|
|
||||||
# def add_employee_to_ceo(self, employee: Union[Agent, Callable]):
|
|
||||||
# self.ceo.employees.append(employee)
|
|
||||||
|
|
||||||
def run(self, task: str):
|
|
||||||
self.ceo_to_department_leaders(task)
|
|
||||||
|
|
||||||
# Then the department leaders to employees
|
|
||||||
|
|
||||||
def ceo_to_department_leaders(self, task: str):
|
|
||||||
orders = self.ceo.run(
|
|
||||||
f"History: {self.conversation.get_str()}\n Your Current Task: {task}"
|
|
||||||
)
|
|
||||||
|
|
||||||
orders = str_to_dict(orders)
|
|
||||||
|
|
||||||
for department in orders["tasks"]["selected_departments"]:
|
|
||||||
department_leader = self.departments[department].leader
|
|
||||||
|
|
||||||
# Get the department leader to break down the task
|
|
||||||
outputs = department_leader.run(
|
|
||||||
orders["tasks"]["selected_leaders"]
|
|
||||||
)
|
|
||||||
|
|
||||||
# Add the department leader's response to the conversation
|
|
||||||
self.conversation.add(
|
|
||||||
role=f"{department_leader.name} from {department}",
|
|
||||||
content=outputs,
|
|
||||||
)
|
|
||||||
@ -1,291 +0,0 @@
|
|||||||
import concurrent.futures
|
|
||||||
from typing import Dict, Optional
|
|
||||||
import secrets
|
|
||||||
import string
|
|
||||||
import uuid
|
|
||||||
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
from swarms import Agent
|
|
||||||
|
|
||||||
import replicate
|
|
||||||
|
|
||||||
from swarms.utils.str_to_dict import str_to_dict
|
|
||||||
|
|
||||||
load_dotenv()
|
|
||||||
|
|
||||||
|
|
||||||
def generate_key(prefix: str = "run") -> str:
|
|
||||||
"""
|
|
||||||
Generates an API key similar to OpenAI's format (sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX).
|
|
||||||
|
|
||||||
Args:
|
|
||||||
prefix (str): The prefix for the API key. Defaults to "sk".
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
str: An API key string in format: prefix-<48 random characters>
|
|
||||||
"""
|
|
||||||
# Create random string of letters and numbers
|
|
||||||
alphabet = string.ascii_letters + string.digits
|
|
||||||
random_part = "".join(secrets.choice(alphabet) for _ in range(28))
|
|
||||||
return f"{prefix}-{random_part}"
|
|
||||||
|
|
||||||
|
|
||||||
def _generate_media(
|
|
||||||
prompt: str = None, modalities: list = None
|
|
||||||
) -> Dict[str, str]:
|
|
||||||
"""
|
|
||||||
Generate media content (images or videos) based on text prompts using AI models.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
prompt (str): Text description of the content to be generated
|
|
||||||
modalities (list): List of media types to generate (e.g., ["image", "video"])
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Dict[str, str]: Dictionary containing file paths of generated media
|
|
||||||
"""
|
|
||||||
if not prompt or not modalities:
|
|
||||||
raise ValueError("Prompt and modalities must be provided")
|
|
||||||
|
|
||||||
input = {"prompt": prompt}
|
|
||||||
results = {}
|
|
||||||
|
|
||||||
def _generate_image(input: Dict) -> str:
|
|
||||||
"""Generate an image and return the file path."""
|
|
||||||
output = replicate.run(
|
|
||||||
"black-forest-labs/flux-dev", input=input
|
|
||||||
)
|
|
||||||
file_paths = []
|
|
||||||
|
|
||||||
for index, item in enumerate(output):
|
|
||||||
unique_id = str(uuid.uuid4())
|
|
||||||
artifact = item.read()
|
|
||||||
file_path = f"output_{unique_id}_{index}.webp"
|
|
||||||
|
|
||||||
with open(file_path, "wb") as file:
|
|
||||||
file.write(artifact)
|
|
||||||
|
|
||||||
file_paths.append(file_path)
|
|
||||||
|
|
||||||
return file_paths
|
|
||||||
|
|
||||||
def _generate_video(input: Dict) -> str:
|
|
||||||
"""Generate a video and return the file path."""
|
|
||||||
output = replicate.run("luma/ray", input=input)
|
|
||||||
unique_id = str(uuid.uuid4())
|
|
||||||
artifact = output.read()
|
|
||||||
file_path = f"output_{unique_id}.mp4"
|
|
||||||
|
|
||||||
with open(file_path, "wb") as file:
|
|
||||||
file.write(artifact)
|
|
||||||
|
|
||||||
return file_path
|
|
||||||
|
|
||||||
for modality in modalities:
|
|
||||||
if modality == "image":
|
|
||||||
results["images"] = _generate_image(input)
|
|
||||||
elif modality == "video":
|
|
||||||
results["video"] = _generate_video(input)
|
|
||||||
else:
|
|
||||||
raise ValueError(f"Unsupported modality: {modality}")
|
|
||||||
|
|
||||||
print(results)
|
|
||||||
|
|
||||||
return results
|
|
||||||
|
|
||||||
|
|
||||||
def generate_media(
|
|
||||||
modalities: list,
|
|
||||||
prompt: Optional[str] = None,
|
|
||||||
count: int = 1,
|
|
||||||
) -> Dict:
|
|
||||||
with concurrent.futures.ThreadPoolExecutor(
|
|
||||||
max_workers=count
|
|
||||||
) as executor:
|
|
||||||
# Create list of identical tasks to run concurrently
|
|
||||||
futures = [
|
|
||||||
executor.submit(
|
|
||||||
_generate_media,
|
|
||||||
prompt=prompt, # Fix: Pass as keyword arguments
|
|
||||||
modalities=modalities,
|
|
||||||
)
|
|
||||||
for _ in range(count)
|
|
||||||
]
|
|
||||||
|
|
||||||
# Wait for all tasks to complete and collect results
|
|
||||||
results = [
|
|
||||||
future.result()
|
|
||||||
for future in concurrent.futures.as_completed(futures)
|
|
||||||
]
|
|
||||||
|
|
||||||
return {"results": results}
|
|
||||||
|
|
||||||
|
|
||||||
tools = [
|
|
||||||
{
|
|
||||||
"type": "function",
|
|
||||||
"function": {
|
|
||||||
"name": "generate_media",
|
|
||||||
"description": "Generate different types of media content (image, video, or music) based on text prompts using AI models.",
|
|
||||||
"parameters": {
|
|
||||||
"type": "object",
|
|
||||||
"properties": {
|
|
||||||
"modality": {
|
|
||||||
"type": "array",
|
|
||||||
"items": {
|
|
||||||
"type": "string",
|
|
||||||
"enum": ["image", "video", "music"],
|
|
||||||
},
|
|
||||||
"description": "The type of media content to generate",
|
|
||||||
},
|
|
||||||
"prompt": {
|
|
||||||
"type": "string",
|
|
||||||
"description": "Text description of the content to be generated. Specialize it for the modality at hand. For example, if you are generating an image, the prompt should be a description of the image you want to see. If you are generating a video, the prompt should be a description of the video you want to see. If you are generating music, the prompt should be a description of the music you want to hear.",
|
|
||||||
},
|
|
||||||
"count": {
|
|
||||||
"type": "integer",
|
|
||||||
"description": "Number of outputs to generate (1-4)",
|
|
||||||
},
|
|
||||||
},
|
|
||||||
"required": [
|
|
||||||
"modality",
|
|
||||||
"prompt",
|
|
||||||
"count",
|
|
||||||
],
|
|
||||||
},
|
|
||||||
},
|
|
||||||
}
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
MEDIA_GENERATION_SYSTEM_PROMPT = """
|
|
||||||
You are an expert AI Media Generation Assistant, specialized in crafting precise and effective prompts for generating images, videos, and music. Your role is to help users create high-quality media content by understanding their requests and translating them into optimal prompts.
|
|
||||||
|
|
||||||
GENERAL GUIDELINES:
|
|
||||||
- Always analyze the user's request carefully to determine the appropriate modality (image, video, or music)
|
|
||||||
- Maintain a balanced level of detail in prompts - specific enough to capture the desired outcome but not overly verbose
|
|
||||||
- Consider the technical limitations and capabilities of AI generation systems
|
|
||||||
- When unclear, ask for clarification about specific details or preferences
|
|
||||||
|
|
||||||
MODALITY-SPECIFIC GUIDELINES:
|
|
||||||
|
|
||||||
1. IMAGE GENERATION:
|
|
||||||
- Structure prompts with primary subject first, followed by style, mood, and technical specifications
|
|
||||||
- Include relevant art styles when specified (e.g., "digital art", "oil painting", "watercolor", "photorealistic")
|
|
||||||
- Consider composition elements (foreground, background, lighting, perspective)
|
|
||||||
- Use specific adjectives for clarity (instead of "beautiful", specify "vibrant", "ethereal", "gritty", etc.)
|
|
||||||
|
|
||||||
Example image prompts:
|
|
||||||
- "A serene Japanese garden at sunset, with cherry blossoms falling, painted in traditional ukiyo-e style, soft pastel colors"
|
|
||||||
- "Cyberpunk cityscape at night, neon lights reflecting in rain puddles, hyper-realistic digital art style"
|
|
||||||
|
|
||||||
2. VIDEO GENERATION:
|
|
||||||
- Describe the sequence of events clearly
|
|
||||||
- Specify camera movements if relevant (pan, zoom, tracking shot)
|
|
||||||
- Include timing and transitions when necessary
|
|
||||||
- Focus on dynamic elements and motion
|
|
||||||
|
|
||||||
Example video prompts:
|
|
||||||
- "Timelapse of a flower blooming in a garden, close-up shot, soft natural lighting, 10-second duration"
|
|
||||||
- "Drone shot flying through autumn forest, camera slowly rising above the canopy, revealing mountains in the distance"
|
|
||||||
|
|
||||||
3. MUSIC GENERATION:
|
|
||||||
- Specify genre, tempo, and mood
|
|
||||||
- Mention key instruments or sounds
|
|
||||||
- Include emotional qualities and intensity
|
|
||||||
- Reference similar artists or styles if relevant
|
|
||||||
|
|
||||||
Example music prompts:
|
|
||||||
- "Calm ambient electronic music with soft synthesizer pads, gentle piano melodies, 80 BPM, suitable for meditation"
|
|
||||||
- "Upbeat jazz fusion track with prominent bass line, dynamic drums, and horn section, inspired by Weather Report"
|
|
||||||
|
|
||||||
COUNT HANDLING:
|
|
||||||
- When multiple outputs are requested (1-4), maintain consistency while introducing subtle variations
|
|
||||||
- For images: Vary composition or perspective while maintaining style
|
|
||||||
- For videos: Adjust camera angles or timing while keeping the core concept
|
|
||||||
- For music: Modify instrument arrangements or tempo while preserving the genre and mood
|
|
||||||
|
|
||||||
PROMPT OPTIMIZATION PROCESS:
|
|
||||||
1. Identify core requirements from user input
|
|
||||||
2. Determine appropriate modality
|
|
||||||
3. Add necessary style and technical specifications
|
|
||||||
4. Adjust detail level based on complexity
|
|
||||||
5. Consider count and create variations if needed
|
|
||||||
|
|
||||||
EXAMPLES OF HANDLING USER REQUESTS:
|
|
||||||
|
|
||||||
User: "I want a fantasy landscape"
|
|
||||||
Assistant response: {
|
|
||||||
"modality": "image",
|
|
||||||
"prompt": "Majestic fantasy landscape with floating islands, crystal waterfalls, and ancient magical ruins, ethereal lighting, digital art style with rich colors",
|
|
||||||
"count": 1
|
|
||||||
}
|
|
||||||
|
|
||||||
User: "Create 3 variations of a peaceful nature scene"
|
|
||||||
Assistant response: {
|
|
||||||
"modality": "image",
|
|
||||||
"prompt": "Tranquil forest clearing with morning mist, sunbeams filtering through ancient trees, photorealistic style with soft natural lighting",
|
|
||||||
"count": 1
|
|
||||||
}
|
|
||||||
|
|
||||||
IMPORTANT CONSIDERATIONS:
|
|
||||||
- Avoid harmful, unethical, or inappropriate content
|
|
||||||
- Respect copyright and intellectual property guidelines
|
|
||||||
- Maintain consistency with brand guidelines when specified
|
|
||||||
- Consider technical limitations of current AI generation systems
|
|
||||||
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Initialize the agent with the new system prompt
|
|
||||||
agent = Agent(
|
|
||||||
agent_name="Media-Generation-Agent",
|
|
||||||
agent_description="AI Media Generation Assistant",
|
|
||||||
system_prompt=MEDIA_GENERATION_SYSTEM_PROMPT,
|
|
||||||
max_loops=1,
|
|
||||||
tools_list_dictionary=tools,
|
|
||||||
output_type="final",
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def create_agent(task: str):
|
|
||||||
output = str_to_dict(agent.run(task))
|
|
||||||
|
|
||||||
print(output)
|
|
||||||
print(type(output))
|
|
||||||
|
|
||||||
prompt = output["prompt"]
|
|
||||||
count = output["count"]
|
|
||||||
modalities = output["modality"]
|
|
||||||
|
|
||||||
output = generate_media(
|
|
||||||
modalities=modalities,
|
|
||||||
prompt=prompt,
|
|
||||||
count=count,
|
|
||||||
)
|
|
||||||
|
|
||||||
run_id = generate_key()
|
|
||||||
|
|
||||||
total_cost = 0
|
|
||||||
|
|
||||||
for modality in modalities:
|
|
||||||
if modality == "image":
|
|
||||||
total_cost += 0.1
|
|
||||||
elif modality == "video":
|
|
||||||
total_cost += 1
|
|
||||||
|
|
||||||
result = {
|
|
||||||
"id": run_id,
|
|
||||||
"success": True,
|
|
||||||
"prompt": prompt,
|
|
||||||
"count": count,
|
|
||||||
"modality": modalities,
|
|
||||||
"total_cost": total_cost,
|
|
||||||
}
|
|
||||||
|
|
||||||
return result
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
task = "Create 3 super kawaii variations of a magical Chinese mountain garden scene in anime style! 🌸✨ Include adorable elements like: cute koi fish swimming in crystal ponds, fluffy clouds floating around misty peaks, tiny pagodas with twinkling lights, and playful pandas hiding in bamboo groves. Make it extra magical with sparkles and soft pastel colors! Create both a video and an image for each variation. Just 1."
|
|
||||||
output = create_agent(task)
|
|
||||||
print("✨ Yay! Here's your super cute creation! ✨")
|
|
||||||
print(output)
|
|
||||||
@ -0,0 +1,173 @@
|
|||||||
|
from swarms import Agent
|
||||||
|
from typing import List
|
||||||
|
|
||||||
|
|
||||||
|
# System prompt for REACT agent
|
||||||
|
REACT_AGENT_PROMPT = """
|
||||||
|
You are a REACT (Reason, Act, Observe) agent designed to solve tasks through an iterative process of reasoning and action. You maintain memory of previous steps to build upon past actions and observations.
|
||||||
|
|
||||||
|
Your process follows these key components:
|
||||||
|
|
||||||
|
1. MEMORY: Review and utilize previous steps
|
||||||
|
- Access and analyze previous observations
|
||||||
|
- Build upon past thoughts and plans
|
||||||
|
- Learn from previous actions
|
||||||
|
- Use historical context to make better decisions
|
||||||
|
|
||||||
|
2. OBSERVE: Analyze current state
|
||||||
|
- Consider both new information and memory
|
||||||
|
- Identify relevant patterns from past steps
|
||||||
|
- Note any changes or progress made
|
||||||
|
- Evaluate success of previous actions
|
||||||
|
|
||||||
|
3. THINK: Process and reason
|
||||||
|
- Combine new observations with historical knowledge
|
||||||
|
- Consider how past steps influence current decisions
|
||||||
|
- Identify patterns and learning opportunities
|
||||||
|
- Plan improvements based on previous outcomes
|
||||||
|
|
||||||
|
4. PLAN: Develop next steps
|
||||||
|
- Create strategies that build on previous success
|
||||||
|
- Avoid repeating unsuccessful approaches
|
||||||
|
- Consider long-term goals and progress
|
||||||
|
- Maintain consistency with previous actions
|
||||||
|
|
||||||
|
5. ACT: Execute with context
|
||||||
|
- Implement actions that progress from previous steps
|
||||||
|
- Build upon successful past actions
|
||||||
|
- Adapt based on learned experiences
|
||||||
|
- Maintain continuity in approach
|
||||||
|
|
||||||
|
For each step, you should:
|
||||||
|
- Reference relevant previous steps
|
||||||
|
- Show how current decisions relate to past actions
|
||||||
|
- Demonstrate learning and adaptation
|
||||||
|
- Maintain coherent progression toward the goal
|
||||||
|
|
||||||
|
Your responses should be structured, logical, and show clear reasoning that builds upon previous steps."""
|
||||||
|
|
||||||
|
# Schema for REACT agent responses
|
||||||
|
react_agent_schema = {
|
||||||
|
"type": "function",
|
||||||
|
"function": {
|
||||||
|
"name": "generate_react_response",
|
||||||
|
"description": "Generates a structured REACT agent response with memory of previous steps",
|
||||||
|
"parameters": {
|
||||||
|
"type": "object",
|
||||||
|
"properties": {
|
||||||
|
"memory_reflection": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Analysis of previous steps and their influence on current thinking",
|
||||||
|
},
|
||||||
|
"observation": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Current state observation incorporating both new information and historical context",
|
||||||
|
},
|
||||||
|
"thought": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Reasoning that builds upon previous steps and current observation",
|
||||||
|
},
|
||||||
|
"plan": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Structured plan that shows progression from previous actions",
|
||||||
|
},
|
||||||
|
"action": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Specific action that builds upon previous steps and advances toward the goal",
|
||||||
|
},
|
||||||
|
},
|
||||||
|
"required": [
|
||||||
|
"memory_reflection",
|
||||||
|
"observation",
|
||||||
|
"thought",
|
||||||
|
"plan",
|
||||||
|
"action",
|
||||||
|
],
|
||||||
|
},
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class ReactAgent:
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
name: str = "react-agent-o1",
|
||||||
|
description: str = "A react agent that uses o1 preview to solve tasks",
|
||||||
|
model_name: str = "openai/gpt-4o",
|
||||||
|
max_loops: int = 1,
|
||||||
|
):
|
||||||
|
self.name = name
|
||||||
|
self.description = description
|
||||||
|
self.model_name = model_name
|
||||||
|
self.max_loops = max_loops
|
||||||
|
|
||||||
|
self.agent = Agent(
|
||||||
|
agent_name=self.name,
|
||||||
|
agent_description=self.description,
|
||||||
|
model_name=self.model_name,
|
||||||
|
max_loops=1,
|
||||||
|
tools_list_dictionary=[react_agent_schema],
|
||||||
|
output_type="final",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Initialize memory for storing steps
|
||||||
|
self.memory: List[str] = []
|
||||||
|
|
||||||
|
def step(self, task: str) -> str:
|
||||||
|
"""Execute a single step of the REACT process.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
task: The task description or current state
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
String response from the agent
|
||||||
|
"""
|
||||||
|
response = self.agent.run(task)
|
||||||
|
print(response)
|
||||||
|
return response
|
||||||
|
|
||||||
|
def run(self, task: str, *args, **kwargs) -> List[str]:
|
||||||
|
"""Run the REACT agent for multiple steps with memory.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
task: The initial task description
|
||||||
|
*args: Additional positional arguments
|
||||||
|
**kwargs: Additional keyword arguments
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of all steps taken as strings
|
||||||
|
"""
|
||||||
|
# Reset memory at the start of a new run
|
||||||
|
self.memory = []
|
||||||
|
|
||||||
|
current_task = task
|
||||||
|
for i in range(self.max_loops):
|
||||||
|
print(f"\nExecuting step {i+1}/{self.max_loops}")
|
||||||
|
step_result = self.step(current_task)
|
||||||
|
print(step_result)
|
||||||
|
|
||||||
|
# Store step in memory
|
||||||
|
self.memory.append(step_result)
|
||||||
|
|
||||||
|
# Update task with previous response and memory context
|
||||||
|
memory_context = (
|
||||||
|
"\n\nMemory of previous steps:\n"
|
||||||
|
+ "\n".join(
|
||||||
|
f"Step {j+1}:\n{step}"
|
||||||
|
for j, step in enumerate(self.memory)
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
current_task = f"Previous response:\n{step_result}\n{memory_context}\n\nContinue with the original task: {task}"
|
||||||
|
|
||||||
|
return self.memory
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == "__main__":
|
||||||
|
# agent = ReactAgent(
|
||||||
|
# max_loops=1
|
||||||
|
# ) # Increased max_loops to see the iteration
|
||||||
|
# result = agent.run(
|
||||||
|
# "Write a short story about a robot that can fly."
|
||||||
|
# )
|
||||||
|
# print(result)
|
||||||
Loading…
Reference in new issue